GPT-6 Luna vs Qwen3.5 9b
GPT-6 Luna averages 69.7% across five Vision Evals tasks and Qwen3.5 9b 56.0% across four (#22 and #44 of 65 models), so GPT-6 Luna leads overall.
- GPT-6 Luna wins all 4 tasks both ran, with its widest lead on object detection (65.5% vs 38.1%).
- GPT-6 Luna is about a fifth of the cost: $0.0004 vs $0.0021 overall average cost per sample.
- GPT-6 Luna is faster: 10.2s vs 41.4s per sample.
Roboflow Vision Evals. Full breakdown below.
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GPT-6 Luna vs Qwen3.5 9b on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-6 Luna vs Qwen3.5 9b Comparison Table
Evals updated October 26, 2026Pricing updated November 7, 2026
| Property | GPT-6 Luna | Qwen3.5 9b |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Mar 2026 |
| Context Window | 1.1M | 262K |
| Parameters | Unknown | 9B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $0.170 |
| Output $/1M | $0.500 | $0.250 |
| Vision Tasks | ||
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| Promptable Concept Segmentation | Demo | Not listed |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 69.7% | 56.0%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0004 | $0.0021 |
| Avg speed / sample | 10.21s | 41.36s |
| By task | ||
| Object Detection (low) | 65.5% ±0.4, Mean of 3 runs, range 65.2 to 66.0 | 38.1% ±5.7, Mean of 3 runs, range 33.5 to 44.9 |
| Object Detection (high) | 68.0% ±0.7, Mean of 3 runs, range 67.3 to 68.6 | – |
| Counting (low) | 71.6% ±0.0, Mean of 3 runs, range 71.6 to 71.6 | 56.8% ±1.4, Mean of 3 runs, range 55.4 to 58.1 |
| Counting (high) | 72.1% ±0.7, Mean of 3 runs, range 71.6 to 73.0 | – |
| Identification (low) | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | – |
| OCR (low) | 59.8% | – |
| by category |
| |
| OCR (high) | 62.5% | – |
| by category |
| |
| Reasoning (low) | 64.2% ±3.0, Mean of 3 runs, range 60.3 to 66.2 | 45.9% ±1.7, Mean of 3 runs, range 44.4 to 47.7 |
| Reasoning (high) | 71.1% ±2.3, Mean of 3 runs, range 68.2 to 72.8 | – |
GPT-6 Luna vs Qwen3.5 9b: Overview
GPT-6 Luna is the fast, cost-efficient tier of OpenAI's GPT-6 model family, sitting below GPT-6 Sol and the larger GPT-6 Astra model that opened the generation. It is a proprietary multimodal transformer that accepts text and image input and returns text, and it exposes an adjustable reasoning effort setting so the same model can run in a low-latency mode or spend additional inference compute on harder problems. OpenAI positions it for high-volume and latency-sensitive workloads such as conversational assistants, classification, and lightweight agentic pipelines, while noting that at higher reasoning effort it handles software engineering and computer-use tasks that previously required a Sol-tier model.
The model supports a context window of roughly 1,050,000 input tokens with a maximum output of 128,000 tokens, which allows long documents, extended agent traces, and large code repositories to be processed in a single request. OpenAI describes the GPT-6 generation as improving factual reliability and adopting a more concise communication style relative to the GPT-5.6 series, and attributes the efficiency of the Sol and Luna tiers to gains in caching and inference rather than to reduced capability. Architecture details, parameter counts, and training data are not published.
Qwen3.5-9B is a 9-billion-parameter multimodal foundation model developed by Alibaba Cloud's Qwen team, released on March 2, 2026 as part of the Qwen3.5 model family.
The model supports vision-language inputs through an early-fusion multimodal architecture built on a dense hybrid foundation of Gated Delta Networks and Gated Attention. It can also operate in a text-only mode by skipping the vision encoder during inference. It provides a 262,144-token context window (extensible to ~1M tokens via YaRN) and is released under the Apache License 2.0. Within the current AI landscape, Qwen3.5-9B offers a strong balance of capability and efficiency, making it well-suited for multimodal assistants, document analysis, long-context reasoning, and developer-deployed agentic systems.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6 Luna performed better. It scores higher on all 4 vision tasks and averages 69.7% (#22 of 65) against 56.0% (#44 of 65) for Qwen3.5 9b. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Object Detection benchmark at low effort, GPT-6 Luna leads with 65.5% against 38.1%. This is the widest gap between the two models across the benchmark's tasks.
GPT-6 Luna is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0004 per sample against $0.0021. Actual costs depend on your image sizes, prompts, and output length.
GPT-6 Luna is faster. Across Roboflow's Vision Evals it averaged 10.2s per inference against 41.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.